From d1d60a976edf3a13a64438e65ac782b7b1281153 Mon Sep 17 00:00:00 2001 From: James Guthrie Date: Wed, 18 Dec 2024 12:16:39 +0100 Subject: [PATCH 1/3] feat: add 'ai.litellm_embed()' for multiple providers --- projects/extension/ai/litellm.py | 35 ++ projects/extension/ai/secrets.py | 20 +- projects/extension/requirements.txt | 4 +- .../extension/sql/idempotent/017-litellm.sql | 58 ++++ .../tests/contents/output16.expected | 4 +- .../tests/contents/output17.expected | 4 +- .../tests/privileges/function.expected | 10 +- projects/extension/tests/test_litellm.py | 311 ++++++++++++++++++ projects/extension/tests/test_voyageai.py | 3 +- 9 files changed, 431 insertions(+), 18 deletions(-) create mode 100644 projects/extension/ai/litellm.py create mode 100644 projects/extension/sql/idempotent/017-litellm.sql create mode 100644 projects/extension/tests/test_litellm.py diff --git a/projects/extension/ai/litellm.py b/projects/extension/ai/litellm.py new file mode 100644 index 000000000..6f4222abe --- /dev/null +++ b/projects/extension/ai/litellm.py @@ -0,0 +1,35 @@ +import litellm +from typing import Optional, Generator + + +def embed( + model: str, + input: list[str], + api_key: str, + user: Optional[str] = None, + dimensions: Optional[int] = None, + timeout: Optional[int] = None, + api_base: Optional[str] = None, + api_version: Optional[str] = None, + api_type: Optional[str] = None, + organization: Optional[str] = None, + **kwargs, +) -> Generator[tuple[int, list[float]], None, None]: + if organization is not None: + litellm.organization = organization + response = litellm.embedding( + model=model, + input=input, + user=user, + dimensions=dimensions, + timeout=timeout, + api_type=api_type, + api_key=api_key, + api_base=api_base, + api_version=api_version, + **kwargs, + ) + if not hasattr(response, "data"): + return None + for idx, obj in enumerate(response["data"]): + yield idx, obj["embedding"] diff --git a/projects/extension/ai/secrets.py b/projects/extension/ai/secrets.py index e889a443e..68a7cef1b 100644 --- a/projects/extension/ai/secrets.py +++ b/projects/extension/ai/secrets.py @@ -24,11 +24,11 @@ def remove_secret_from_cache(sd_cache: dict[str, str], secret_name: str): def get_secret( plpy, - secret: Optional[str], - secret_name: Optional[str], - secret_name_default: str, - sd_cache: Optional[dict[str, str]], -) -> str: + secret: Optional[str] = None, + secret_name: Optional[str] = None, + secret_name_default: Optional[str] = None, + sd_cache: Optional[dict[str, str]] = None, +) -> str | None: if secret is not None: return secret @@ -36,15 +36,9 @@ def get_secret( secret_name = secret_name_default if secret_name is None or secret_name == "": - plpy.error("secret_name is required") - - secret = reveal_secret(plpy, secret_name, sd_cache) - if secret is None: - plpy.error(f"missing {secret_name} secret") - # This line should never be reached, but it's here to make the type checker happy. - return "" + return None - return secret + return reveal_secret(plpy, secret_name, sd_cache) def check_secret_permissions(plpy, secret_name: str) -> bool: diff --git a/projects/extension/requirements.txt b/projects/extension/requirements.txt index 1a6360509..2b2be89cd 100644 --- a/projects/extension/requirements.txt +++ b/projects/extension/requirements.txt @@ -1,4 +1,4 @@ -openai==1.44.0 +openai==1.56.0 tiktoken==0.7.0 ollama==0.4.5 anthropic==0.29.0 @@ -6,3 +6,5 @@ cohere==5.5.8 backoff==2.2.1 voyageai==0.3.1 datasets==3.1.0 +litellm==1.55.4 +google-cloud-aiplatform==1.74.0 # required for vertexAI (don't know why litellm doesn't include this) diff --git a/projects/extension/sql/idempotent/017-litellm.sql b/projects/extension/sql/idempotent/017-litellm.sql new file mode 100644 index 000000000..62d02ff9d --- /dev/null +++ b/projects/extension/sql/idempotent/017-litellm.sql @@ -0,0 +1,58 @@ +------------------------------------------------------------------------------- +-- litellm_embed +-- generate an embedding from a text value +create or replace function ai.litellm_embed +( model text +, input_text text +, api_key text default null +, api_key_name text default null +, extra_options jsonb default null +) returns @extschema:vector@.vector +as $python$ + #ADD-PYTHON-LIB-DIR + import ai.litellm + import ai.secrets + options = {} + if extra_options is not None: + import json + options = {k: v for k, v in json.loads(extra_options).items()} + + api_key_resolved = ai.secrets.get_secret(plpy, api_key, api_key_name, None, SD) + for tup in ai.litellm.embed(model, [input_text], api_key=api_key_resolved, **options): + return tup[1] +$python$ +language plpython3u immutable parallel safe security invoker +set search_path to pg_catalog, pg_temp +; + +------------------------------------------------------------------------------- +-- litellm_embed +-- generate embeddings from an array of text values +create or replace function ai.litellm_embed +( model text +, input_texts text[] +, api_key text default null +, api_key_name text default null +, extra_options jsonb default null +) returns table +( "index" int +, embedding @extschema:vector@.vector +) +as $python$ + #ADD-PYTHON-LIB-DIR + import ai.litellm + import ai.secrets + options = {} + if extra_options is not None: + import json + options = {k: v for k, v in json.loads(extra_options).items()} + + plpy.log("options", options) + + api_key_resolved = ai.secrets.get_secret(plpy, api_key, api_key_name, None, SD) + for tup in ai.litellm.embed(model, input_texts, api_key=api_key_resolved, **options): + yield tup +$python$ +language plpython3u immutable parallel safe security invoker +set search_path to pg_catalog, pg_temp +; diff --git a/projects/extension/tests/contents/output16.expected b/projects/extension/tests/contents/output16.expected index ef557f1bc..44f130d1a 100644 --- a/projects/extension/tests/contents/output16.expected +++ b/projects/extension/tests/contents/output16.expected @@ -34,6 +34,8 @@ CREATE EXTENSION function ai.indexing_diskann(integer,text,integer,integer,double precision,integer,integer,boolean) function ai.indexing_hnsw(integer,text,integer,integer,boolean) function ai.indexing_none() + function ai.litellm_embed(text,text,text,text,jsonb) + function ai.litellm_embed(text,text[],text,text,jsonb) function ai.load_dataset_multi_txn(text,text,text,name,name,text,jsonb,integer,integer,integer,jsonb) function ai.load_dataset(text,text,text,name,name,text,jsonb,integer,integer,jsonb) function ai.ollama_chat_complete(text,jsonb,text,text,jsonb) @@ -94,7 +96,7 @@ CREATE EXTENSION table ai.vectorizer_errors view ai.secret_permissions view ai.vectorizer_status -(90 rows) +(92 rows) Table "ai._secret_permissions" Column | Type | Collation | Nullable | Default | Storage | Compression | Stats target | Description diff --git a/projects/extension/tests/contents/output17.expected b/projects/extension/tests/contents/output17.expected index 35c69d53b..d555a5ee4 100644 --- a/projects/extension/tests/contents/output17.expected +++ b/projects/extension/tests/contents/output17.expected @@ -34,6 +34,8 @@ CREATE EXTENSION function ai.indexing_diskann(integer,text,integer,integer,double precision,integer,integer,boolean) function ai.indexing_hnsw(integer,text,integer,integer,boolean) function ai.indexing_none() + function ai.litellm_embed(text,text,text,text,jsonb) + function ai.litellm_embed(text,text[],text,text,jsonb) function ai.load_dataset_multi_txn(text,text,text,name,name,text,jsonb,integer,integer,integer,jsonb) function ai.load_dataset(text,text,text,name,name,text,jsonb,integer,integer,jsonb) function ai.ollama_chat_complete(text,jsonb,text,text,jsonb) @@ -108,7 +110,7 @@ CREATE EXTENSION type ai.vectorizer_status[] view ai.secret_permissions view ai.vectorizer_status -(104 rows) +(106 rows) Table "ai._secret_permissions" Column | Type | Collation | Nullable | Default | Storage | Compression | Stats target | Description diff --git a/projects/extension/tests/privileges/function.expected b/projects/extension/tests/privileges/function.expected index 3b19b8a8a..66a3d24a1 100644 --- a/projects/extension/tests/privileges/function.expected +++ b/projects/extension/tests/privileges/function.expected @@ -220,6 +220,14 @@ f | bob | execute | no | ai | indexing_none() f | fred | execute | no | ai | indexing_none() f | jill | execute | YES | ai | indexing_none() + f | alice | execute | YES | ai | litellm_embed(model text, input_text text, api_key text, api_key_name text, extra_options jsonb) + f | bob | execute | no | ai | litellm_embed(model text, input_text text, api_key text, api_key_name text, extra_options jsonb) + f | fred | execute | no | ai | litellm_embed(model text, input_text text, api_key text, api_key_name text, extra_options jsonb) + f | jill | execute | YES | ai | litellm_embed(model text, input_text text, api_key text, api_key_name text, extra_options jsonb) + f | alice | execute | YES | ai | litellm_embed(model text, input_texts text[], api_key text, api_key_name text, extra_options jsonb) + f | bob | execute | no | ai | litellm_embed(model text, input_texts text[], api_key text, api_key_name text, extra_options jsonb) + f | fred | execute | no | ai | litellm_embed(model text, input_texts text[], api_key text, api_key_name text, extra_options jsonb) + f | jill | execute | YES | ai | litellm_embed(model text, input_texts text[], api_key text, api_key_name text, extra_options jsonb) f | alice | execute | YES | ai | load_dataset(name text, config_name text, split text, schema_name name, table_name name, if_table_exists text, field_types jsonb, batch_size integer, max_batches integer, kwargs jsonb) f | bob | execute | no | ai | load_dataset(name text, config_name text, split text, schema_name name, table_name name, if_table_exists text, field_types jsonb, batch_size integer, max_batches integer, kwargs jsonb) f | fred | execute | no | ai | load_dataset(name text, config_name text, split text, schema_name name, table_name name, if_table_exists text, field_types jsonb, batch_size integer, max_batches integer, kwargs jsonb) @@ -328,5 +336,5 @@ f | bob | execute | no | ai | voyageai_embed(model text, input_texts text[], input_type text, api_key text, api_key_name text) f | fred | execute | no | ai | voyageai_embed(model text, input_texts text[], input_type text, api_key text, api_key_name text) f | jill | execute | YES | ai | voyageai_embed(model text, input_texts text[], input_type text, api_key text, api_key_name text) -(328 rows) +(336 rows) diff --git a/projects/extension/tests/test_litellm.py b/projects/extension/tests/test_litellm.py new file mode 100644 index 000000000..41e745b3e --- /dev/null +++ b/projects/extension/tests/test_litellm.py @@ -0,0 +1,311 @@ +import os +from typing import List + +import psycopg +import pytest + + +@pytest.fixture() +def cur() -> psycopg.Cursor: + with psycopg.connect("postgres://test@127.0.0.1:5432/test") as con: + with con.cursor() as cur: + yield cur + + +models = [ + { + "id": "openai", + "name": "openai/text-embedding-3-small", + "dimensions": 1536, + "api_key_name": "OPENAI_API_KEY", + "exception": "OpenAIException - The api_key client option must be set", + "input_types": [], + }, + { + "id": "voyage", + "name": "voyage/voyage-3-lite", + "dimensions": 512, + "api_key_name": "VOYAGE_API_KEY", + "exception": "VoyageException - The api_key client option must be set", + "input_types": ["query", "document"], + }, + { + "id": "cohere", + "name": "cohere/embed-english-v3.0", + "dimensions": 1024, + "api_key_name": "COHERE_API_KEY", + "exception": """CohereException - {"message":"no api key supplied"}""", + "input_types": [ + "search_query", + "search_document", + "classification", + "clustering", + ], + }, + { + "id": "mistral", + "name": "mistral/mistral-embed", + "dimensions": 1024, + "api_key_name": "MISTRAL_API_KEY", + "exception": "MistralException - The api_key client option must be set", + "input_types": [], + }, + { + "id": "huggingface", + "name": "huggingface/microsoft/codebert-base", + "dimensions": 768, + "api_key_name": "HUGGINGFACE_API_KEY", + "exception": """HuggingfaceException - {"error":"Please log in or use a HF access token"}""", + "input_types": [], + }, +] + +ids = [model["id"] for model in models] + + +def model_keys(*args): + return [[i[k] for k in args] for i in models] + + +@pytest.mark.parametrize("name,exception", model_keys("name", "exception"), ids=ids) +def test_litellm_embed_fails_without_secret( + cur: psycopg.Cursor, name: str, exception: str +): + with pytest.raises(psycopg.errors.ExternalRoutineException, match=exception) as _: + cur.execute( + """ + select vector_dims + ( + ai.litellm_embed + ( %s + , 'hello world' + ) + ) + """, + (name,), + ) + + +@pytest.mark.parametrize( + "name,dimensions,api_key_name", + model_keys("name", "dimensions", "api_key_name"), + ids=ids, +) +def test_litellm_embed_with_api_key_via_guc( + cur: psycopg.Cursor, name: str, dimensions: int, api_key_name: str +): + api_key_value = os.getenv(api_key_name) + if api_key_value is None: + pytest.skip(f"environment variable '{api_key_name}' unset") + cur.execute( + f"select set_config('ai.{api_key_name.lower()}', %s, false) is not null", + (api_key_value,), + ) + cur.execute( + """ + select vector_dims + ( + ai.litellm_embed + ( %s + , 'hello world' + , api_key_name => %s + ) + ) + """, + ( + name, + api_key_name, + ), + ) + actual = cur.fetchone()[0] + assert actual == dimensions + + +@pytest.mark.parametrize( + "name,dimensions,api_key_name", + model_keys("name", "dimensions", "api_key_name"), + ids=ids, +) +def test_litellm_embed( + cur: psycopg.Cursor, name: str, dimensions: int, api_key_name: str +): + api_key_value = os.getenv(api_key_name) + if api_key_value is None: + pytest.skip(f"environment variable '{api_key_name}' unset") + cur.execute( + """ + select vector_dims + ( + ai.litellm_embed + ( %s + , 'hello world' + , api_key=>%s + ) + ) + """, + ( + name, + api_key_value, + ), + ) + actual = cur.fetchone()[0] + assert actual == dimensions + + +@pytest.mark.parametrize( + "name,dimensions,api_key_name,input_types", + model_keys("name", "dimensions", "api_key_name", "input_types"), + ids=ids, +) +def test_litellm_embed_input_types( + cur: psycopg.Cursor, + name: str, + dimensions: int, + api_key_name: str, + input_types: List[str], +): + api_key_value = os.getenv(api_key_name) + if api_key_value is None: + pytest.skip(f"environment variable '{api_key_name}' unset") + if len(input_types) == 0: + pytest.skip("no input types for model") + for input_type in input_types: + cur.execute( + """ + select vector_dims + ( + ai.litellm_embed + ( %s + , 'hello world' + , api_key=>%s + , extra_options => jsonb_build_object('input_type', %s::text) + ) + ) + """, + ( + name, + api_key_value, + input_type, + ), + ) + actual = cur.fetchone()[0] + assert actual == dimensions + + +@pytest.mark.parametrize( + "name,dimensions,api_key_name", + model_keys("name", "dimensions", "api_key_name"), + ids=ids, +) +def test_litellm_embed_with_multiple_inputs( + cur: psycopg.Cursor, name: str, dimensions: int, api_key_name: str +): + api_key_value = os.getenv(api_key_name) + if api_key_value is None: + pytest.skip(f"environment variable '{api_key_name}' unset") + cur.execute( + """ + select count(*) from ai.litellm_embed + ( %s + , ARRAY['hello world', 'hello bob'] + , api_key=>%s + ) + """, + ( + name, + api_key_value, + ), + ) + result = cur.fetchone()[0] + assert result == 2 + + +def load_secrets(*args): + secrets = {} + for name in args: + secrets[name] = os.getenv(name) + if secrets[name] is None: + pytest.skip(f"environment variable '{name}' unset") + return secrets + + +def test_litellm_embed_azure_openai(cur: psycopg.Cursor): + secrets = load_secrets( + "AZURE_OPENAI_API_KEY", "AZURE_OPENAI_API_BASE", "AZURE_OPENAI_API_VERSION" + ) + cur.execute( + """ + select vector_dims + ( + ai.litellm_embed + ( 'azure/text-embedding-3-small' + , 'hello world' + , api_key=> %s + , extra_options => jsonb_build_object( + 'api_base', %s::text, + 'api_version', %s::text + ) + ) + ) + """, + ( + secrets["AZURE_OPENAI_API_KEY"], + secrets["AZURE_OPENAI_API_BASE"], + secrets["AZURE_OPENAI_API_VERSION"], + ), + ) + actual = cur.fetchone()[0] + assert actual == 1536 + + +def test_litellm_embed_bedrock(cur: psycopg.Cursor): + secrets = load_secrets( + "AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY", "AWS_REGION_NAME" + ) + cur.execute( + """ + select vector_dims + ( + ai.litellm_embed + ( 'amazon.titan-embed-text-v1' + , 'hello world' + , extra_options => jsonb_build_object( + 'aws_access_key_id', %s::text, + 'aws_secret_access_key', %s::text, + 'aws_region_name', %s::text + ) + ) + ) + """, + ( + secrets["AWS_ACCESS_KEY_ID"], + secrets["AWS_SECRET_ACCESS_KEY"], + secrets["AWS_REGION_NAME"], + ), + ) + actual = cur.fetchone()[0] + assert actual == 1536 + + +@pytest.mark.skip(reason="testing vertex is hard") +def test_litellm_embed_vertex_ai(cur: psycopg.Cursor): + secrets = load_secrets("VERTEX_CREDENTIALS") + cur.execute( + """ + select vector_dims + ( + ai.litellm_embed + ( 'vertex_ai/textembedding-gecko' + , 'hello world' + , extra_options => jsonb_build_object( + 'vertex_credentials', %s::text, + 'vertex_project', 'sandbox-james', + 'vertex_location', 'us-central1' + ) + ) + ) + """, + (secrets["VERTEX_CREDENTIALS"],), + ) + actual = cur.fetchone()[0] + assert actual == 768 diff --git a/projects/extension/tests/test_voyageai.py b/projects/extension/tests/test_voyageai.py index 457183d88..2221f64de 100644 --- a/projects/extension/tests/test_voyageai.py +++ b/projects/extension/tests/test_voyageai.py @@ -25,7 +25,8 @@ def cur() -> psycopg.Cursor: def test_voyageai_fails_without_secret(cur, voyageai_api_key): with pytest.raises( - psycopg.errors.InternalError_, match="missing VOYAGE_API_KEY secret" + psycopg.errors.ExternalRoutineException, + match="voyageai.error.AuthenticationError: No API key provided.", ) as _: cur.execute( """ From 357f0e9726fdd7d37138ca2735510d625453a166 Mon Sep 17 00:00:00 2001 From: James Guthrie Date: Fri, 20 Dec 2024 16:34:28 +0100 Subject: [PATCH 2/3] WIP: litellm vectorizer TODOs: - tests for all integrations - fine-tune configuration parameters - fine-tune huggingface (cold-start?) --- .../sql/idempotent/008-embedding.sql | 26 + .../tests/contents/output16.expected | 1 + .../tests/contents/output17.expected | 3 +- .../tests/privileges/function.expected | 6 +- projects/extension/tests/test_litellm.py | 42 +- projects/pgai/pgai/cli.py | 6 +- .../pgai/vectorizer/embedders/__init__.py | 1 + .../pgai/pgai/vectorizer/embedders/litellm.py | 132 ++++ projects/pgai/pgai/vectorizer/vectorizer.py | 4 +- projects/pgai/pyproject.toml | 3 +- ...nglish_v3_0_1024_items_1_batch_size_1.yaml | 62 ++ ...nglish_v3_0_1024_items_4_batch_size_2.yaml | 126 ++++ ...odebert_base_768_items_1_batch_size_1.yaml | 327 +++++++++ ...odebert_base_768_items_4_batch_size_2.yaml | 584 +++++++++++++++ ...stral_embed_1024_items_1_batch_size_1.yaml | 187 +++++ ...stral_embed_1024_items_4_batch_size_2.yaml | 691 ++++++++++++++++++ ...ing_3_small_1536_items_1_batch_size_1.yaml | 204 ++++++ ...ing_3_small_1536_items_4_batch_size_2.yaml | 617 ++++++++++++++++ ...oyage_3_lite_512_items_1_batch_size_1.yaml | 40 + ...oyage_3_lite_512_items_4_batch_size_2.yaml | 78 ++ projects/pgai/tests/vectorizer/conftest.py | 1 - .../tests/vectorizer/test_vectorizer_cli.py | 201 +++-- projects/pgai/uv.lock | 288 +++++++- 23 files changed, 3530 insertions(+), 100 deletions(-) create mode 100644 projects/pgai/pgai/vectorizer/embedders/litellm.py create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_cohere_embed_english_v3_0_1024_items_1_batch_size_1.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_cohere_embed_english_v3_0_1024_items_4_batch_size_2.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_huggingface_microsoft_codebert_base_768_items_1_batch_size_1.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_huggingface_microsoft_codebert_base_768_items_4_batch_size_2.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_mistral_mistral_embed_1024_items_1_batch_size_1.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_mistral_mistral_embed_1024_items_4_batch_size_2.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_openai_text_embedding_3_small_1536_items_1_batch_size_1.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_openai_text_embedding_3_small_1536_items_4_batch_size_2.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_voyage_voyage_3_lite_512_items_1_batch_size_1.yaml create mode 100644 projects/pgai/tests/vectorizer/cassettes/embedding_litellm_voyage_voyage_3_lite_512_items_4_batch_size_2.yaml diff --git a/projects/extension/sql/idempotent/008-embedding.sql b/projects/extension/sql/idempotent/008-embedding.sql index e03e21ed5..a5b20ef01 100644 --- a/projects/extension/sql/idempotent/008-embedding.sql +++ b/projects/extension/sql/idempotent/008-embedding.sql @@ -74,6 +74,30 @@ $func$ language plpgsql immutable security invoker set search_path to pg_catalog, pg_temp ; +------------------------------------------------------------------------------- +-- embedding_litellm +create or replace function ai.embedding_litellm +( model pg_catalog.text +, dimensions pg_catalog.int4 +, api_key_name pg_catalog.text default null +, extra_options pg_catalog.jsonb default null +) returns pg_catalog.jsonb +as $func$ +begin + return json_object + ( 'implementation': 'litellm' + , 'config_type': 'embedding' + , 'model': model + , 'dimensions': dimensions + , 'api_key_name': api_key_name + , 'extra_options': extra_options + absent on null + ); +end +$func$ language plpgsql immutable security invoker +set search_path to pg_catalog, pg_temp +; + ------------------------------------------------------------------------------- -- _validate_embedding create or replace function ai._validate_embedding(config pg_catalog.jsonb) returns void @@ -98,6 +122,8 @@ begin -- ok when 'voyageai' then -- ok + when 'litellm' then + -- ok else if _implementation is null then raise exception 'embedding implementation not specified'; diff --git a/projects/extension/tests/contents/output16.expected b/projects/extension/tests/contents/output16.expected index 44f130d1a..327266c6c 100644 --- a/projects/extension/tests/contents/output16.expected +++ b/projects/extension/tests/contents/output16.expected @@ -20,6 +20,7 @@ CREATE EXTENSION function ai.create_vectorizer(regclass,name,jsonb,jsonb,jsonb,jsonb,jsonb,jsonb,name,name,name,name,name,name,name[],boolean) function ai.disable_vectorizer_schedule(integer) function ai.drop_vectorizer(integer,boolean) + function ai.embedding_litellm(text,integer,text,jsonb) function ai.embedding_ollama(text,integer,text,jsonb,text) function ai.embedding_openai(text,integer,text,text) function ai.embedding_voyageai(text,integer,text,text) diff --git a/projects/extension/tests/contents/output17.expected b/projects/extension/tests/contents/output17.expected index d555a5ee4..db357abe1 100644 --- a/projects/extension/tests/contents/output17.expected +++ b/projects/extension/tests/contents/output17.expected @@ -20,6 +20,7 @@ CREATE EXTENSION function ai.create_vectorizer(regclass,name,jsonb,jsonb,jsonb,jsonb,jsonb,jsonb,name,name,name,name,name,name,name[],boolean) function ai.disable_vectorizer_schedule(integer) function ai.drop_vectorizer(integer,boolean) + function ai.embedding_litellm(text,integer,text,jsonb) function ai.embedding_ollama(text,integer,text,jsonb,text) function ai.embedding_openai(text,integer,text,text) function ai.embedding_voyageai(text,integer,text,text) @@ -110,7 +111,7 @@ CREATE EXTENSION type ai.vectorizer_status[] view ai.secret_permissions view ai.vectorizer_status -(106 rows) +(107 rows) Table "ai._secret_permissions" Column | Type | Collation | Nullable | Default | Storage | Compression | Stats target | Description diff --git a/projects/extension/tests/privileges/function.expected b/projects/extension/tests/privileges/function.expected index 66a3d24a1..d8932f1d4 100644 --- a/projects/extension/tests/privileges/function.expected +++ b/projects/extension/tests/privileges/function.expected @@ -164,6 +164,10 @@ f | bob | execute | no | ai | drop_vectorizer(vectorizer_id integer, drop_all boolean) f | fred | execute | no | ai | drop_vectorizer(vectorizer_id integer, drop_all boolean) f | jill | execute | YES | ai | drop_vectorizer(vectorizer_id integer, drop_all boolean) + f | alice | execute | YES | ai | embedding_litellm(model text, dimensions integer, api_key_name text, extra_options jsonb) + f | bob | execute | no | ai | embedding_litellm(model text, dimensions integer, api_key_name text, extra_options jsonb) + f | fred | execute | no | ai | embedding_litellm(model text, dimensions integer, api_key_name text, extra_options jsonb) + f | jill | execute | YES | ai | embedding_litellm(model text, dimensions integer, api_key_name text, extra_options jsonb) f | alice | execute | YES | ai | embedding_ollama(model text, dimensions integer, base_url text, options jsonb, keep_alive text) f | bob | execute | no | ai | embedding_ollama(model text, dimensions integer, base_url text, options jsonb, keep_alive text) f | fred | execute | no | ai | embedding_ollama(model text, dimensions integer, base_url text, options jsonb, keep_alive text) @@ -336,5 +340,5 @@ f | bob | execute | no | ai | voyageai_embed(model text, input_texts text[], input_type text, api_key text, api_key_name text) f | fred | execute | no | ai | voyageai_embed(model text, input_texts text[], input_type text, api_key text, api_key_name text) f | jill | execute | YES | ai | voyageai_embed(model text, input_texts text[], input_type text, api_key text, api_key_name text) -(336 rows) +(340 rows) diff --git a/projects/extension/tests/test_litellm.py b/projects/extension/tests/test_litellm.py index 41e745b3e..b114621bf 100644 --- a/projects/extension/tests/test_litellm.py +++ b/projects/extension/tests/test_litellm.py @@ -1,8 +1,9 @@ import os -from typing import List +from typing import List, Any import psycopg import pytest +import json @pytest.fixture() @@ -19,6 +20,7 @@ def cur() -> psycopg.Cursor: "dimensions": 1536, "api_key_name": "OPENAI_API_KEY", "exception": "OpenAIException - The api_key client option must be set", + "extra_options": {}, "input_types": [], }, { @@ -26,7 +28,8 @@ def cur() -> psycopg.Cursor: "name": "voyage/voyage-3-lite", "dimensions": 512, "api_key_name": "VOYAGE_API_KEY", - "exception": "VoyageException - The api_key client option must be set", + "exception": """VoyageException - {"detail":"Provided API key is invalid."}""", + "extra_options": {}, "input_types": ["query", "document"], }, { @@ -35,6 +38,7 @@ def cur() -> psycopg.Cursor: "dimensions": 1024, "api_key_name": "COHERE_API_KEY", "exception": """CohereException - {"message":"no api key supplied"}""", + "extra_options": {}, "input_types": [ "search_query", "search_document", @@ -48,6 +52,7 @@ def cur() -> psycopg.Cursor: "dimensions": 1024, "api_key_name": "MISTRAL_API_KEY", "exception": "MistralException - The api_key client option must be set", + "extra_options": {}, "input_types": [], }, { @@ -56,6 +61,7 @@ def cur() -> psycopg.Cursor: "dimensions": 768, "api_key_name": "HUGGINGFACE_API_KEY", "exception": """HuggingfaceException - {"error":"Please log in or use a HF access token"}""", + "extra_options": {"wait_for_model": True}, "input_types": [], }, ] @@ -87,12 +93,16 @@ def test_litellm_embed_fails_without_secret( @pytest.mark.parametrize( - "name,dimensions,api_key_name", - model_keys("name", "dimensions", "api_key_name"), + "name,dimensions,api_key_name,extra_options", + model_keys("name", "dimensions", "api_key_name", "extra_options"), ids=ids, ) def test_litellm_embed_with_api_key_via_guc( - cur: psycopg.Cursor, name: str, dimensions: int, api_key_name: str + cur: psycopg.Cursor, + name: str, + dimensions: int, + api_key_name: str, + extra_options: dict[Any, Any], ): api_key_value = os.getenv(api_key_name) if api_key_value is None: @@ -109,25 +119,27 @@ def test_litellm_embed_with_api_key_via_guc( ( %s , 'hello world' , api_key_name => %s + , extra_options => %s::jsonb ) ) """, - ( - name, - api_key_name, - ), + (name, api_key_name, json.dumps(extra_options)), ) actual = cur.fetchone()[0] assert actual == dimensions @pytest.mark.parametrize( - "name,dimensions,api_key_name", - model_keys("name", "dimensions", "api_key_name"), + "name,dimensions,api_key_name,extra_options", + model_keys("name", "dimensions", "api_key_name", "extra_options"), ids=ids, ) def test_litellm_embed( - cur: psycopg.Cursor, name: str, dimensions: int, api_key_name: str + cur: psycopg.Cursor, + name: str, + dimensions: int, + api_key_name: str, + extra_options: dict[Any, Any], ): api_key_value = os.getenv(api_key_name) if api_key_value is None: @@ -140,13 +152,11 @@ def test_litellm_embed( ( %s , 'hello world' , api_key=>%s + , extra_options => %s::jsonb ) ) """, - ( - name, - api_key_value, - ), + (name, api_key_value, json.dumps(extra_options)), ) actual = cur.fetchone()[0] assert actual == dimensions diff --git a/projects/pgai/pgai/cli.py b/projects/pgai/pgai/cli.py index 4c839f305..b749db55e 100644 --- a/projects/pgai/pgai/cli.py +++ b/projects/pgai/pgai/cli.py @@ -131,7 +131,11 @@ def get_vectorizer(db_url: str, vectorizer_id: int) -> Vectorizer: return vectorizer -def run_vectorizer(db_url: str, vectorizer: Vectorizer, concurrency: int) -> None: +def run_vectorizer( + db_url: str, + vectorizer: Vectorizer, + concurrency: int, +) -> None: async def run_workers( db_url: str, vectorizer: Vectorizer, concurrency: int ) -> list[int]: diff --git a/projects/pgai/pgai/vectorizer/embedders/__init__.py b/projects/pgai/pgai/vectorizer/embedders/__init__.py index 8eda5c1b4..de1ba9dfc 100644 --- a/projects/pgai/pgai/vectorizer/embedders/__init__.py +++ b/projects/pgai/pgai/vectorizer/embedders/__init__.py @@ -1,3 +1,4 @@ +from .litellm import LiteLLM as LiteLLM from .ollama import Ollama as Ollama from .openai import OpenAI as OpenAI from .voyageai import VoyageAI as VoyageAI diff --git a/projects/pgai/pgai/vectorizer/embedders/litellm.py b/projects/pgai/pgai/vectorizer/embedders/litellm.py new file mode 100644 index 000000000..c64944f92 --- /dev/null +++ b/projects/pgai/pgai/vectorizer/embedders/litellm.py @@ -0,0 +1,132 @@ +from collections.abc import Sequence +from functools import cached_property +from typing import Any, Literal + +import litellm +from litellm import EmbeddingResponse as LiteLLMEmbeddingResponse # type: ignore +from litellm import InMemoryCache # type: ignore +from pydantic import BaseModel +from typing_extensions import override + +from ..embeddings import ( + BatchApiCaller, + Embedder, + EmbeddingResponse, + EmbeddingVector, + StringDocument, + Usage, + logger, +) + + +# TODO: remove this when this issue is fixed upstream: https://github.com/BerriAI/litellm/issues/7667 +# Note: we did consider building an event-loop aware in-memory cache, but the +# additional complexity doesn't seem to be worth it. +class NoopCache(InMemoryCache): + """ + A no-op cache + + This class exists because litellm's internals cause http clients to be + re-used across different event loops. The httpx client does not like this, + which causes exceptions to be thrown. Note: Not all http clients throw + exceptions, so we are being overly cautious with this approach. + """ + + def __init__(self): + super().__init__() + + @override + def get_cache(self, key: Any, **kwargs: Any): + return None + + @override + def set_cache(self, key: Any, value: Any, **kwargs: Any): + pass + + +litellm.in_memory_llm_clients_cache = NoopCache() + + +class UnknownProviderError(Exception): + pass + + +class LiteLLM(BaseModel, Embedder): + """ + Embedder that uses LiteLLM to embed documents into vector representations. + + Attributes: + implementation (Literal["litellm"]): The literal identifier for this + implementation. + model (str): The name of the embedding model. + api_key_name (str): The API key name. + extra_options (dict): Additional litellm-specific options + """ + + implementation: Literal["litellm"] + model: str + api_key_name: str | None = None + extra_options: dict[str, Any] = {} + + @override + async def embed(self, documents: list[str]) -> Sequence[EmbeddingVector]: + """ + Embeds a list of documents into vectors using LiteLLM. + + Args: + documents (list[str]): A list of documents to be embedded. + + Returns: + Sequence[EmbeddingVector | ChunkEmbeddingError]: The embeddings or + errors for each document. + """ + await logger.adebug(f"Chunks produced: {len(documents)}") + return await self._batcher.batch_chunks_and_embed(documents) + + @cached_property + def _batcher(self) -> BatchApiCaller[StringDocument]: + return BatchApiCaller(self._max_chunks_per_batch(), self.call_embed_api) + + @override + def _max_chunks_per_batch(self) -> int: + print(f"model: {self.model}") + _, custom_llm_provider, _, _ = litellm.get_llm_provider(self.model) # type: ignore + match custom_llm_provider: + case "cohere": + return 96 # see https://docs.cohere.com/v1/reference/embed#request.body.texts + case "openai": + return 2048 # see https://platform.openai.com/docs/api-reference/embeddings/create + case "azure": + return 1024 # TODO: unknown + case "bedrock": + return 2048 # TODO: unknown + case "huggingface": + return 1024 # TODO: unknown + case "mistral": + return 1024 # TODO: unknown + case "vertex": + return 1024 # TODO: unknown + case "voyage": + return 128 # see https://docs.voyageai.com/reference/embeddings-api + case _: + raise UnknownProviderError(custom_llm_provider) + + async def call_embed_api(self, documents: str | list[str]) -> EmbeddingResponse: + # Without `suppress_debug_info`, LiteLLM writes the following into stdout: + # Provider List: https://docs.litellm.ai/docs/providers + # This is useless, and confusing, so we suppress it. + litellm.suppress_debug_info = True + response: LiteLLMEmbeddingResponse = await litellm.aembedding( # type: ignore + model=self.model, input=documents, **self.extra_options + ) + usage = ( + Usage( + prompt_tokens=response.usage.prompt_tokens, + total_tokens=response.usage.total_tokens, + ) + if response.usage is not None + else Usage(prompt_tokens=0, total_tokens=0) + ) + return EmbeddingResponse( + embeddings=[d["embedding"] for d in response["data"]], usage=usage + ) diff --git a/projects/pgai/pgai/vectorizer/vectorizer.py b/projects/pgai/pgai/vectorizer/vectorizer.py index d57c249f5..62cf1f587 100644 --- a/projects/pgai/pgai/vectorizer/vectorizer.py +++ b/projects/pgai/pgai/vectorizer/vectorizer.py @@ -22,7 +22,7 @@ LangChainCharacterTextSplitter, LangChainRecursiveCharacterTextSplitter, ) -from .embedders import Ollama, OpenAI, VoyageAI +from .embedders import LiteLLM, Ollama, OpenAI, VoyageAI from .embeddings import ChunkEmbeddingError from .formatting import ChunkValue, PythonTemplate from .processing import ProcessingDefault @@ -75,7 +75,7 @@ class Config: """ version: str - embedding: OpenAI | Ollama | VoyageAI + embedding: OpenAI | Ollama | VoyageAI | LiteLLM processing: ProcessingDefault chunking: ( LangChainCharacterTextSplitter | LangChainRecursiveCharacterTextSplitter diff --git a/projects/pgai/pyproject.toml b/projects/pgai/pyproject.toml index 2851c44f3..6129119e4 100644 --- a/projects/pgai/pyproject.toml +++ b/projects/pgai/pyproject.toml @@ -25,6 +25,7 @@ dependencies = [ "datadog_lambda>=6.9,<7.0", "pytimeparse>=1.1,<2.0", "voyageai>=0.3.1,<0.4.0", + "litellm>=1.57.5,<1.58.0", ] classifiers = [ "License :: OSI Approved :: PostgreSQL License", @@ -112,7 +113,7 @@ dev-dependencies = [ "ruff==0.6.9", "pytest==8.3.2", "python-dotenv==1.0.1", - "vcrpy==6.0.1", + "vcrpy==7.0.0", "pyright==1.1.385", "psycopg[binary]==3.2.1", "testcontainers==4.8.1", diff --git a/projects/pgai/tests/vectorizer/cassettes/embedding_litellm_cohere_embed_english_v3_0_1024_items_1_batch_size_1.yaml b/projects/pgai/tests/vectorizer/cassettes/embedding_litellm_cohere_embed_english_v3_0_1024_items_1_batch_size_1.yaml new file mode 100644 index 000000000..d6d16af7e --- /dev/null +++ b/projects/pgai/tests/vectorizer/cassettes/embedding_litellm_cohere_embed_english_v3_0_1024_items_1_batch_size_1.yaml @@ -0,0 +1,62 @@ +interactions: +- request: + body: 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+ headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '12851' + Via: + - 1.1 google + content-type: + - application/json + date: + - Thu, 09 Jan 2025 13:51:53 GMT + server: + - uvicorn + x-api-warning: + - '' + status: + code: 200 + message: OK +version: 1 diff --git a/projects/pgai/tests/vectorizer/conftest.py b/projects/pgai/tests/vectorizer/conftest.py index 23b593535..cd0495aa2 100644 --- a/projects/pgai/tests/vectorizer/conftest.py +++ b/projects/pgai/tests/vectorizer/conftest.py @@ -52,7 +52,6 @@ def vcr_(): filter_headers=["authorization"], match_on=["method", "scheme", "host", "port", "path", "query", "body"], before_record_response=remove_set_cookie_header, - ignore_hosts=["huggingface.co"], ) diff --git a/projects/pgai/tests/vectorizer/test_vectorizer_cli.py b/projects/pgai/tests/vectorizer/test_vectorizer_cli.py index 04f97154c..b85cb8b11 100644 --- a/projects/pgai/tests/vectorizer/test_vectorizer_cli.py +++ b/projects/pgai/tests/vectorizer/test_vectorizer_cli.py @@ -1,5 +1,7 @@ +import json import logging import os +import re import subprocess import time from collections.abc import Generator @@ -117,37 +119,6 @@ def source_table( return table_name -@pytest.fixture -def configured_openai_vectorizer_id( - source_table: str, - cli_db: tuple[TestDatabase, Connection], - test_params: tuple[int, int, int, str, str], -) -> int: - """Creates and configures a vectorizer for testing""" - _, concurrency, batch_size, chunking, formatting = test_params - _, conn = cli_db - - with conn.cursor(row_factory=dict_row) as cur: - # Create vectorizer - cur.execute(f""" - SELECT ai.create_vectorizer( - '{source_table}'::regclass, - embedding => ai.embedding_openai( - 'text-embedding-ada-002', - 1536, - api_key_name => 'OPENAI_API_KEY' - ), - chunking => ai.{chunking}, - formatting => ai.{formatting}, - processing => ai.processing_default(batch_size => {batch_size}, - concurrency => {concurrency}) - ) - """) # type: ignore - vectorizer_id: int = int(cur.fetchone()["create_vectorizer"]) # type: ignore - - return vectorizer_id - - @pytest.fixture(scope="session") def ollama_connection_url(): # If the OLLAMA_HOST environment variable is set, we assume that the user @@ -164,14 +135,12 @@ def ollama_connection_url(): yield ollama.get_endpoint() -@pytest.fixture -def configured_ollama_vectorizer_id( +def configure_vectorizer( source_table: str, cli_db: tuple[TestDatabase, Connection], test_params: tuple[int, int, int, str, str], - ollama_connection_url: str, -) -> int: - """Creates and configures an ollama vectorizer for testing""" + embedding: str, +): _, concurrency, batch_size, chunking, formatting = test_params _, conn = cli_db @@ -180,11 +149,7 @@ def configured_ollama_vectorizer_id( cur.execute(f""" SELECT ai.create_vectorizer( '{source_table}'::regclass, - embedding => ai.embedding_ollama( - 'nomic-embed-text', - 768, - base_url => '{ollama_connection_url}' - ), + embedding => ai.{embedding}, chunking => ai.{chunking}, formatting => ai.{formatting}, processing => ai.processing_default(batch_size => {batch_size}, @@ -197,33 +162,46 @@ def configured_ollama_vectorizer_id( @pytest.fixture -def configured_voyageai_vectorizer_id( +def configured_openai_vectorizer_id( source_table: str, cli_db: tuple[TestDatabase, Connection], test_params: tuple[int, int, int, str, str], ) -> int: - """Creates and configures a VoyageAI vectorizer for testing""" - _, concurrency, batch_size, chunking, formatting = test_params - _, conn = cli_db + """Creates and configures a vectorizer for testing""" + return configure_vectorizer( + source_table, + cli_db, + test_params, + "embedding_openai('text-embedding-ada-002', 1536)", + ) - with conn.cursor(row_factory=dict_row) as cur: - # Create vectorizer - cur.execute(f""" - SELECT ai.create_vectorizer( - '{source_table}'::regclass, - embedding => ai.embedding_voyageai( - 'voyage-3-lite', - 512 - ), - chunking => ai.{chunking}, - formatting => ai.{formatting}, - processing => ai.processing_default(batch_size => {batch_size}, - concurrency => {concurrency}) - ) - """) # type: ignore - vectorizer_id: int = int(cur.fetchone()["create_vectorizer"]) # type: ignore - return vectorizer_id +@pytest.fixture +def configured_ollama_vectorizer_id( + source_table: str, + cli_db: tuple[TestDatabase, Connection], + test_params: tuple[int, int, int, str, str], + ollama_connection_url: str, +) -> int: + """Creates and configures an ollama vectorizer for testing""" + return configure_vectorizer( + source_table, + cli_db, + test_params, + f"embedding_ollama('nomic-embed-text', 768, base_url => '{ollama_connection_url}')", # noqa: E501 Line too long + ) + + +@pytest.fixture +def configured_voyageai_vectorizer_id( + source_table: str, + cli_db: tuple[TestDatabase, Connection], + test_params: tuple[int, int, int, str, str], +) -> int: + """Creates and configures a VoyageAI vectorizer for testing""" + return configure_vectorizer( + source_table, cli_db, test_params, "embedding_voyageai('voyage-3-lite', 512)" + ) @pytest.fixture @@ -452,11 +430,14 @@ def test_invalid_api_key_error( cli_db: tuple[TestDatabase, Connection], cli_db_url: str, configured_openai_vectorizer_id: int, + test_params: Any, # noqa vcr_: Any, ): """Test that worker handles invalid API key appropriately""" _, conn = cli_db + os.environ["OPENAI_API_KEY"] = "invalid" + # When running the worker and getting an invalid api key response with vcr_.use_cassette("test_invalid_api_key_error.yaml"): try: @@ -1009,3 +990,99 @@ def test_vectorization_successful_with_null_contents( with conn.cursor(row_factory=dict_row) as cur: cur.execute("SELECT count(*) as count FROM blog_embedding_store;") assert cur.fetchone()["count"] == 0 # type: ignore + + +@pytest.mark.parametrize( + "test_params", + [ + ( + 1, + 1, + 1, + "chunking_character_text_splitter('content')", + "formatting_python_template('$chunk')", + ), + ( + 4, + 2, + 2, + "chunking_character_text_splitter('content')", + "formatting_python_template('$chunk')", + ), + ], +) +@pytest.mark.parametrize( + "embedding", + [ + ("openai/text-embedding-3-small", 1536, {}), + ("voyage/voyage-3-lite", 512, {}), + ("mistral/mistral-embed", 1024, {}), + ("cohere/embed-english-v3.0", 1024, {}), + ( + "huggingface/microsoft/codebert-base", + 768, + {"wait_for_model": True, "use_cache": False}, + ), + ], +) +def test_litellm_vectorizer( + source_table: str, + cli_db: tuple[TestDatabase, Connection], + cli_db_url: str, + embedding: tuple[str, int, dict[str, Any]], + vcr_: Any, + test_params: tuple[int, int, int, str, str], +): + model, dimensions, extra_options = embedding + function = "embedding_litellm" + + embedding_str = f"{function}('{model}', {dimensions}, extra_options => '{json.dumps(extra_options)}'::jsonb)" # noqa: E501 Line too long + + vectorizer_id = configure_vectorizer( + source_table, cli_db, test_params, embedding_str + ) + + num_items, concurrency, batch_size, _, _ = test_params + _, conn = cli_db + # Insert pre-existing embedding for first item + with conn.cursor() as cur: + cur.execute( + sql.SQL(""" + INSERT INTO + blog_embedding_store(embedding_uuid, id, chunk_seq, chunk, embedding) + VALUES (gen_random_uuid(), 1, 1, 'post_1', + array_fill(0, ARRAY[{0}])::vector) + """).format(dimensions) + ) + + stripped_model = re.sub(r"\W", "_", model) + + # When running the worker with cassette matching original test params + cassette = f"{function}_{stripped_model}_{dimensions}_items_{num_items}_batch_size_{batch_size}.yaml" # noqa: E501 Line too long + logging.getLogger("vcr").setLevel(logging.DEBUG) + with vcr_.use_cassette(cassette): + result = CliRunner().invoke( + vectorizer_worker, + [ + "--db-url", + cli_db_url, + "--once", + "--vectorizer-id", + str(vectorizer_id), + "--concurrency", + str(concurrency), + "--log-level", + "debug", + ], + catch_exceptions=False, + ) + + if result.exception: + print(result.output) + + assert not result.exception + assert result.exit_code == 0 + + with conn.cursor(row_factory=dict_row) as cur: + cur.execute("SELECT count(*) as count FROM blog_embedding_store;") + assert cur.fetchone()["count"] == num_items # type: ignore diff --git a/projects/pgai/uv.lock b/projects/pgai/uv.lock index d9a17daf4..276c1ecca 100644 --- a/projects/pgai/uv.lock +++ b/projects/pgai/uv.lock @@ -1,12 +1,18 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version < '3.11'", - "python_full_version == '3.11.*'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", - "python_full_version >= '3.13' and python_full_version < '4'", - "python_full_version >= '4'", + "python_full_version < '3.11' and platform_python_implementation == 'PyPy'", + "python_full_version < '3.11' and platform_python_implementation != 'PyPy'", + "python_full_version == '3.11.*' and platform_python_implementation == 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}}-${{ github.ref }} + cancel-in-progress: true + jobs: build-and-test-extension: services: diff --git a/projects/extension/tests/contents/output16.expected b/projects/extension/tests/contents/output16.expected index 327266c6c..52e25b1bd 100644 --- a/projects/extension/tests/contents/output16.expected +++ b/projects/extension/tests/contents/output16.expected @@ -97,7 +97,7 @@ CREATE EXTENSION table ai.vectorizer_errors view ai.secret_permissions view ai.vectorizer_status -(92 rows) +(93 rows) Table "ai._secret_permissions" Column | Type | Collation | Nullable | Default | Storage | Compression | Stats target | Description diff --git a/projects/extension/tests/test_litellm.py b/projects/extension/tests/test_litellm.py index b114621bf..cd224c5f8 100644 --- a/projects/extension/tests/test_litellm.py +++ b/projects/extension/tests/test_litellm.py @@ -77,6 +77,8 @@ def model_keys(*args): def test_litellm_embed_fails_without_secret( cur: psycopg.Cursor, name: str, exception: str ): + if name == "huggingface/microsoft/codebert-base": + pytest.skip("we can't get this test to work reliably in CI") with pytest.raises(psycopg.errors.ExternalRoutineException, match=exception) as _: cur.execute( """ diff --git a/projects/pgai/tests/vectorizer/test_vectorizer_cli.py b/projects/pgai/tests/vectorizer/test_vectorizer_cli.py index b85cb8b11..7547fb7fb 100644 --- a/projects/pgai/tests/vectorizer/test_vectorizer_cli.py +++ b/projects/pgai/tests/vectorizer/test_vectorizer_cli.py @@ -1014,14 +1014,15 @@ def test_vectorization_successful_with_null_contents( @pytest.mark.parametrize( "embedding", [ - ("openai/text-embedding-3-small", 1536, {}), - ("voyage/voyage-3-lite", 512, {}), - ("mistral/mistral-embed", 1024, {}), - ("cohere/embed-english-v3.0", 1024, {}), + ("openai/text-embedding-3-small", 1536, {}, "OPENAI_API_KEY"), + ("voyage/voyage-3-lite", 512, {}, "VOYAGE_API_KEY"), + ("mistral/mistral-embed", 1024, {}, "MISTRAL_API_KEY"), + ("cohere/embed-english-v3.0", 1024, {}, "COHERE_API_KEY"), ( "huggingface/microsoft/codebert-base", 768, {"wait_for_model": True, "use_cache": False}, + "HUGGINGFACE_API_KEY", ), ], ) @@ -1029,13 +1030,16 @@ def test_litellm_vectorizer( source_table: str, cli_db: tuple[TestDatabase, Connection], cli_db_url: str, - embedding: tuple[str, int, dict[str, Any]], + embedding: tuple[str, int, dict[str, Any], str], vcr_: Any, test_params: tuple[int, int, int, str, str], ): - model, dimensions, extra_options = embedding + model, dimensions, extra_options, api_key_name = embedding function = "embedding_litellm" + if api_key_name not in os.environ: + pytest.skip(f"environment variable '{api_key_name}' unset") + embedding_str = f"{function}('{model}', {dimensions}, extra_options => '{json.dumps(extra_options)}'::jsonb)" # noqa: E501 Line too long vectorizer_id = configure_vectorizer(